Hazy Environment Research Articles

Learning intensity and detail mapping parameters for dehazing

State-of-the-art methods for removing haze from a single image rely on a haze image formation model and the inversion problem is solved by estimating medium transmission and global atmospheric light. In this paper, we propose a hazy image enhancement framework, called Intensity Mapping and Detail Mapping(IMDM), for dehazing, which can strike a good balance in enhancing details and preserving color fidelity. We propose two mapping fucntions to respectively process the detail and intensity components of the hazy image instead of jointly tackling the two parts. Compared with joint tackling of these two kinds of information, respective processings are more advantagous in stretching the contrast and highlight the details. For obtaining the optimal parameters of mapping functions, we specially set up an image dataset, which consists of numbers of pairs of hazy and the corresponding ground truth images, for the training process. In order to simulate hazy images in the real world as much as possible, hazy images in this dataset are captured in an artificial hazy environment. With the learned parameters, details in the hazy image can be clearly restored without causing color infidelity. Experimental results demonstrate that the proposed method (called IMDM) is superior to both the enhancement and model-based methods in terms of improving the visibility and preserving color fidelity.

Using a hybrid of fuzzy theory and neural network filter for single image dehazing

When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and improve the visual quality, this paper presents an efficient method combining the fuzzy inference system and the neural network filter to solve image dehazing. During dehazing, the fuzzy inference system is adopted to estimate the variations in light attenuation, and the erosion of morphological operation and the neural network filter are used to eliminate the halation and achieve optimization in transmission map refinement. Finally, the brightest 1% of the atmospheric light is utilized to calculate the color vector of atmospheric light to eliminate color cast. Experimental results indicate that the proposed method is superior to other dehazing methods.

Preparation of Nanofibrous Metal-Organic Framework Filters for Efficient Air Pollution Control.

Environmental challenges especially air pollution (particulate matter (PM) and toxic gases) pose serious threats to public health globally. Metal-organic frameworks (MOFs) are crystalline materials with high porosity, tunable pore size, and rich functionalities, holding the promise for poisonous pollutants capture. Here, nanocrystals of four unique MOF structures are processed into nanofibrous filters (noted as MOFilter) with high MOF loadings (up to 60 wt %). The MOFilters show high PM removal efficiencies up to 88.33 ± 1.52% and 89.67 ± 1.33% for PM2.5 and PM10, respectively, in the hazy environment, and the performance remains largely unchanged over 48 h of continuous filtration. For the first time, the interactions between such porous crystalline material and particulate pollutants were explored. These thin MOFilters can further selectively capture and retain SO2 when exposed to a stream of SO2/N2 mixture, and their hierarchical nanostructures can easily permeate fresh air at high gas flow rate with the pressure drop <20 Pa.

Image haze removal using a hybrid of fuzzy inference system and weighted estimation

The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts.

Fog and Hazy Simulation for outside Lighting Study

With the high humidity caused by the urban microclimate, the visibility is greatly reduced due to the fog weather as well as the hazy weather formed by the accumulation of pollutant and vapor. In order to evaluate how the fog and haze weather affects outside lighting, a hazy simulation environment is introduced in this paper. An ultrasonic humidifier is used to produce droplets for fog simulation. Haze is simulated by using air blower to blow plant ashes. Transmittance of several different light sources under the simulated fog and haze conditions is shown. The results can be used to study outside lighting.

A Simplified Method of Assessing the Progress of a Cataract Based on Deterioration in Color Discrimination by Scattering Light

Cataract is one of the most typical age-related eye diseases. As a cataract progress, the crystalline lens becomes hazier and visual ability deteriorates. Since the main cause of visual impairment is light scattered by the hazy lens, both brighter environments and hazier lenses worsen visual ability. In this study, error scores of 50 Hue test were measured for pseudo-cataract subjects (young observer with foggy filters of known as haze factor) when the intensity of scattering light was increased. The rate of increase in error score of 50 Hue test was larger with higher haze factor. We propose, as an easy method for assessing the progress of a cataract, the haze estimation of elderly crystalline lens using the obtained error scores of 50 Hue test as a function of the intensity of scattering light.

Quantum Darwinism in a Mixed Environment

Quantum Darwinism recognizes that we-the observers-acquire our information about the "systems of interest" indirectly from their imprints on the environment. Here, we show that information about a system can be acquired from a mixed-state, or hazy, environment, but the storage capacity of an environment fragment is suppressed by its initial entropy. In the case of good decoherence, the mutual information between the system and the fragment is given solely by the fragment's entropy increase. For fairly mixed environments, this means a reduction by a factor 1-h, where h is the haziness of the environment, i.e., the initial entropy of an environment qubit. Thus, even such hazy environments eventually reveal the state of the system, although now the intercepted environment fragment must be larger by approximately (1-h)(-1) to gain the same information about the system.

Hydrocarbon Development in the Iraqi Kurdistan Region Petroleum Sector

This paper examines the challenges that Post-Saddam Iraqi Kurdistan faces in developing a sustainable petroleum sector. The newly established national constitution was a significant step that left as many petroleum questions unanswered, as answered. The constitution, not only separated branches of government, but established Federalism as its lodestar. Still, due to unresolved issues over regional and national control over petroleum resources, oil companies function in a hazy legal environment. The first part of the paper discusses the past and current state of the Kurdish region's oil development, and notes future prospects for development. The second part draws on constitutional and legal sources to analyze the division of responsibilities among the regions and the national government. This paper shows that firm guidelines are essential to encourage investment and expeditiously rebuild Iraq's shattered Petroleum sector.